Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerModel") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7ce5af4e09eb9b4d93767995c959ff4593c8f62e30be9a04e34d6c318f8f00a7
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size 162240
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